Objective¶
Workflow¶
- Requried python packages
- Paths/Lookups/Constants
- Load dataset
Requried python packages¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
import scipy.stats as st
Paths/Lookups/Constants¶
In [3]:
input_path = "../data/data.csv"
excel_input_path = "../data/input_data.xlsx"
output_path = "../outputs/"
Load datasets¶
In [4]:
data = pd.read_excel(excel_input_path)
In [13]:
data.head()
Out[13]:
| Short description | Description | Caller | Assignment group | |
|---|---|---|---|---|
| 0 | login issue | -verified user details.(employee# & manager na... | spxjnwir pjlcoqds | GRP_0 |
| 1 | outlook | \r\n\r\nreceived from: hmjdrvpb.komuaywn@gmail... | hmjdrvpb komuaywn | GRP_0 |
| 2 | cant log in to vpn | \r\n\r\nreceived from: eylqgodm.ybqkwiam@gmail... | eylqgodm ybqkwiam | GRP_0 |
| 3 | unable to access hr_tool page | unable to access hr_tool page | xbkucsvz gcpydteq | GRP_0 |
| 4 | skype error | skype error | owlgqjme qhcozdfx | GRP_0 |
Target distribution¶
In [14]:
data["Assignment group"].value_counts()
Out[14]:
GRP_0 3976
GRP_8 661
GRP_24 289
GRP_12 257
GRP_9 252
...
GRP_67 1
GRP_64 1
GRP_61 1
GRP_70 1
GRP_35 1
Name: Assignment group, Length: 74, dtype: int64
In [17]:
target_distribution = data["Assignment group"].value_counts().rename_axis("Assignment_group").reset_index(name='counts')
In [19]:
target_distribution["percentage"] = target_distribution["counts"] / len(data) * 100.0
In [20]:
target_distribution.head()
Out[20]:
| Assignment_group | counts | percentage | |
|---|---|---|---|
| 0 | GRP_0 | 3976 | 46.776471 |
| 1 | GRP_8 | 661 | 7.776471 |
| 2 | GRP_24 | 289 | 3.400000 |
| 3 | GRP_12 | 257 | 3.023529 |
| 4 | GRP_9 | 252 | 2.964706 |
In [21]:
## Save file for visuvalization
# file_name = "target_distribution.csv"
# target_distribution.to_csv(output_path + file_name, index=False)
Data preprocessing¶
In [6]:
data.columns.values
Out[6]:
array(['Short description', 'Description', 'Caller', 'Assignment group'],
dtype=object)
We have 3 features to assign the target class, out of which the featuare caller is not having any high level significance so not considering it for the model building.
Handle Non cases¶
In [5]:
## Checking the non cases in all the fields of data
data.isna().sum()
Out[5]:
Short description 8 Description 1 Caller 0 Assignment group 0 dtype: int64
We are having very limited amount of nan in the given data, in the short description filed we are having high number of nan case which are very in limited percentage when we consider with total data.
So removing these nan case to make the data nan free data(not having the nan cases at all).
In [7]:
data.dropna(inplace = True)
Cross checking if we are still having any nan cases
In [8]:
data.isna().sum()
Out[8]:
Short description 0 Description 0 Caller 0 Assignment group 0 dtype: int64
In [20]:
import re
# Removing the extra spaces lowering the text
# Removing unwanted characters
def text_preprocessing(text):
text = text.lower()
text = re.sub('[^\w\s]',' ', text)
text = re.sub('\s+',' ',text)
text = re.sub('\d+', '', text)
text = text.strip()
return text
In [10]:
data["Description"][0]
Out[10]:
'-verified user details.(employee# & manager name)\r\n-checked the user name in ad and reset the password.\r\n-advised the user to login and check.\r\n-caller confirmed that he was able to login.\r\n-issue resolved.'
In [22]:
text_preprocessing(data["Description"][0])
Out[22]:
'verified user details employee manager name checked the user name in ad and reset the password advised the user to login and check caller confirmed that he was able to login issue resolved'
Let's apply the text_preprocessing function on both the Description and Short description¶
In [25]:
data["updated_description"] = list(map(text_preprocessing, data["Description"]))
In [27]:
data["updated_short_description"] = list(map(text_preprocessing, data["Short description"]))
In [28]:
data.head()
Out[28]:
| Short description | Description | Caller | Assignment group | updated_description | updated_short_description | |
|---|---|---|---|---|---|---|
| 0 | login issue | -verified user details.(employee# & manager na... | spxjnwir pjlcoqds | GRP_0 | verified user details employee manager name ch... | login issue |
| 1 | outlook | \r\n\r\nreceived from: hmjdrvpb.komuaywn@gmail... | hmjdrvpb komuaywn | GRP_0 | received from hmjdrvpb komuaywn gmail com hell... | outlook |
| 2 | cant log in to vpn | \r\n\r\nreceived from: eylqgodm.ybqkwiam@gmail... | eylqgodm ybqkwiam | GRP_0 | received from eylqgodm ybqkwiam gmail com hi i... | cant log in to vpn |
| 3 | unable to access hr_tool page | unable to access hr_tool page | xbkucsvz gcpydteq | GRP_0 | unable to access hr_tool page | unable to access hr_tool page |
| 4 | skype error | skype error | owlgqjme qhcozdfx | GRP_0 | skype error | skype error |
Merge both Short description and Description¶
From the data we observed cases where short description is repeated in description with out any changes, So while merging both short description and description we need check is the short description and description are same or not.
If both the description are same then use only one, else merge both. </b>
In [29]:
def merge_descriptions(short, large):
descrition = large
if short != large:
descrition = short + " " + large
return descrition
In [31]:
merge_descriptions("dfsdfdsfs", "dfsdfdsfs")
Out[31]:
'dfsdfdsfs'
In [33]:
merge_descriptions("dfsdfdsfs", "dfsdfdsfsddf s")
Out[33]:
'dfsdfdsfs dfsdfdsfsddf s'
In [34]:
data["updated_description"][0], data["updated_short_description"][0]
Out[34]:
('verified user details employee manager name checked the user name in ad and reset the password advised the user to login and check caller confirmed that he was able to login issue resolved',
'login issue')
In [36]:
merge_descriptions(data["updated_short_description"][0], data["updated_description"][0])
Out[36]:
'login issue verified user details employee manager name checked the user name in ad and reset the password advised the user to login and check caller confirmed that he was able to login issue resolved'
Let's apply the merge_descriptions function on updated description to get the final version description¶
In [40]:
data["final_description"] = list(map(merge_descriptions,
data["updated_short_description"], data["updated_description"]))
In [42]:
data[["updated_short_description", "updated_description", "final_description"]].head()
Out[42]:
| updated_short_description | updated_description | final_description | |
|---|---|---|---|
| 0 | login issue | verified user details employee manager name ch... | login issue verified user details employee man... |
| 1 | outlook | received from hmjdrvpb komuaywn gmail com hell... | outlook received from hmjdrvpb komuaywn gmail ... |
| 2 | cant log in to vpn | received from eylqgodm ybqkwiam gmail com hi i... | cant log in to vpn received from eylqgodm ybqk... |
| 3 | unable to access hr_tool page | unable to access hr_tool page | unable to access hr_tool page |
| 4 | skype error | skype error | skype error |
Let's create the model dataset aside¶
In [45]:
model_data = data[["final_description", 'Assignment group']].copy()
In [46]:
model_data.head()
Out[46]:
| final_description | Assignment group | |
|---|---|---|
| 0 | login issue verified user details employee man... | GRP_0 |
| 1 | outlook received from hmjdrvpb komuaywn gmail ... | GRP_0 |
| 2 | cant log in to vpn received from eylqgodm ybqk... | GRP_0 |
| 3 | unable to access hr_tool page | GRP_0 |
| 4 | skype error | GRP_0 |
Now we are ready with modeling data which is preprocessed.
Analysis on target data (assignment group)¶
Let's the see the assignment group description again.
In [47]:
target_distribution = model_data["Assignment group"].value_counts().rename_axis("Assignment_group").reset_index(name='counts')
target_distribution["percentage"] = target_distribution["counts"] / len(model_data.index) * 100.0
In [60]:
target_distribution['cumulative_percentage'] = target_distribution['percentage'].cumsum(axis = 0)
In [154]:
target_distribution.head(25)
Out[154]:
| Assignment_group | counts | percentage | cumulative_percentage | |
|---|---|---|---|---|
| 0 | GRP_0 | 3968 | 46.731834 | 46.731834 |
| 1 | GRP_8 | 661 | 7.784713 | 54.516547 |
| 2 | GRP_24 | 289 | 3.403604 | 57.920151 |
| 3 | GRP_12 | 257 | 3.026734 | 60.946885 |
| 4 | GRP_9 | 252 | 2.967848 | 63.914733 |
| 5 | GRP_2 | 241 | 2.838299 | 66.753033 |
| 6 | GRP_19 | 215 | 2.532093 | 69.285125 |
| 7 | GRP_3 | 200 | 2.355435 | 71.640561 |
| 8 | GRP_6 | 184 | 2.167000 | 73.807561 |
| 9 | GRP_13 | 145 | 1.707690 | 75.515251 |
| 10 | GRP_10 | 140 | 1.648805 | 77.164056 |
| 11 | GRP_5 | 129 | 1.519256 | 78.683312 |
| 12 | GRP_14 | 118 | 1.389707 | 80.073018 |
| 13 | GRP_25 | 116 | 1.366152 | 81.439171 |
| 14 | GRP_33 | 107 | 1.260158 | 82.699329 |
| 15 | GRP_4 | 100 | 1.177718 | 83.877046 |
| 16 | GRP_29 | 97 | 1.142386 | 85.019432 |
| 17 | GRP_18 | 88 | 1.036391 | 86.055824 |
| 18 | GRP_16 | 85 | 1.001060 | 87.056884 |
| 19 | GRP_17 | 81 | 0.953951 | 88.010835 |
| 20 | GRP_31 | 69 | 0.812625 | 88.823460 |
| 21 | GRP_7 | 68 | 0.800848 | 89.624308 |
| 22 | GRP_34 | 61 | 0.718408 | 90.342716 |
| 23 | GRP_26 | 56 | 0.659522 | 91.002238 |
| 24 | GRP_40 | 45 | 0.529973 | 91.532211 |
In [52]:
len(target_distribution["Assignment_group"].unique())
Out[52]:
74
We are having 74 unique target classes so we are converting the low percentage targetes to single class.
In [70]:
## We will filter the classes which came less than 1% population
In [83]:
less_popular_target_class = list(target_distribution[target_distribution["cumulative_percentage"] > 99]["Assignment_group"])
In [84]:
len(less_popular_target_class)
Out[84]:
25
In [72]:
target_distribution[target_distribution["Assignment_group"] == 'GRP_52']
Out[72]:
| Assignment_group | counts | percentage | cumulative_percentage | |
|---|---|---|---|---|
| 49 | GRP_52 | 9 | 0.105995 | 99.08138 |
In [85]:
less_popular_target_class
Out[85]:
['GRP_52', 'GRP_51', 'GRP_55', 'GRP_46', 'GRP_59', 'GRP_49', 'GRP_43', 'GRP_66', 'GRP_32', 'GRP_63', 'GRP_58', 'GRP_38', 'GRP_56', 'GRP_68', 'GRP_69', 'GRP_57', 'GRP_54', 'GRP_71', 'GRP_72', 'GRP_70', 'GRP_35', 'GRP_73', 'GRP_61', 'GRP_64', 'GRP_67']
We will convert all the less popular target class to single class
In [90]:
def update_target_class(target):
updated_target = target
if target in less_popular_target_class:
updated_target = "less_pop"
return updated_target
In [91]:
update_target_class("GRP_67")
Out[91]:
'less_pop'
In [92]:
model_data["updated_target"] = list(map(update_target_class, model_data["Assignment group"]))
In [89]:
len(model_data["Assignment group"].unique()), len(model_data["updated_target"].unique())
Out[89]:
(74, 50)
In [149]:
targets_list = list(model_data["updated_target"].unique())
In [93]:
model_data.head()
Out[93]:
| final_description | Assignment group | updated_target | |
|---|---|---|---|
| 0 | login issue verified user details employee man... | GRP_0 | GRP_0 |
| 1 | outlook received from hmjdrvpb komuaywn gmail ... | GRP_0 | GRP_0 |
| 2 | cant log in to vpn received from eylqgodm ybqk... | GRP_0 | GRP_0 |
| 3 | unable to access hr_tool page | GRP_0 | GRP_0 |
| 4 | skype error | GRP_0 | GRP_0 |
Let's build the models with final_description as feature and updated_target as target¶
In [102]:
import nltk
from nltk.stem import SnowballStemmer
from nltk.tokenize import word_tokenize
from nltk import pos_tag
In [96]:
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
In [98]:
# Tokenize the description and removing the stopwords.
model_data['desc_tokens'] = model_data.apply(lambda row: nltk.word_tokenize(row['final_description']), axis=1)
model_data['desc_tokens'].apply(lambda x: [item for item in x if item not in stop_words])
Out[98]:
0 [login, issue, verified, user, details, employ...
1 [outlook, received, hmjdrvpb, komuaywn, gmail,...
2 [cant, log, vpn, received, eylqgodm, ybqkwiam,...
3 [unable, access, hr_tool, page]
4 [skype, error]
...
8495 [emails, coming, zz, mail, received, avglmrts,...
8496 [telephony_software, issue]
8497 [vip, windows, password, reset, tifpdchb, pedx...
8498 [machine, nã, estã, funcionando, unable, acces...
8499 [mehreren, pc, lassen, sich, verschiedene, prg...
Name: desc_tokens, Length: 8491, dtype: object
In [99]:
snow = SnowballStemmer('english')
In [100]:
def apply_stemmed_approach(tokens):
stemmed = []
for token in tokens:
stemmed.append(snow.stem(token))
return stemmed
In [101]:
model_data['desc_tokens_stemmed'] = model_data['desc_tokens'].apply(lambda v: apply_stemmed_approach(v))
Apply post tagging¶
In [103]:
model_data['tokens_desc_postags'] = model_data['desc_tokens_stemmed'].apply(lambda v: pos_tag(v))
Apply lemmatization using WordNetLemmatizer¶
In [104]:
from nltk.stem import WordNetLemmatizer
# nltk.download('wordnet')
lemmatizer = WordNetLemmatizer()
from nltk.corpus import wordnet
def get_wordnet_pos(treebank_tag):
if treebank_tag.startswith('J'):
return wordnet.ADJ
elif treebank_tag.startswith('V'):
return wordnet.VERB
elif treebank_tag.startswith('N'):
return wordnet.NOUN
elif treebank_tag.startswith('R'):
return wordnet.ADV
else:
return None # for easy if-statement
def get_lemmatized_list(list):
output = []
for token, tag in list:
wntag = get_wordnet_pos(tag)
if wntag is None:# not supply tag in case of None
lemma = lemmatizer.lemmatize(token)
else:
lemma = lemmatizer.lemmatize(token, pos=wntag)
output.append(lemma)
return output
In [105]:
model_data['tokens_desc_lemmatization'] = model_data['tokens_desc_postags'].apply(lambda v: get_lemmatized_list(v))
Bag of words¶
In [106]:
def get_bag_of_words(words_lists):
bag_of_words = ""
for word_list in words_lists:
for word in word_list:
bag_of_words = bag_of_words + " " + word
return bag_of_words
Creating wordclouds¶
In [111]:
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
stopwords = STOPWORDS
In [112]:
def get_wordcloud_image(target_class):
target_data = model_data.copy()
target_data[target_data["updated_target"].apply(lambda x : x ==target_class)]
wordcloud_data = WordCloud(stopwords = stop_words, max_words=500,
background_color="white",width=800, height=400).generate(
get_bag_of_words(target_data['tokens_desc_lemmatization'].values))
plt.figure( figsize=(20,10) )
plt.imshow(wordcloud_data,interpolation='bilinear')
plt.axis("off")
plt.show()
In [113]:
get_wordcloud_image('GRP_0')
In [114]:
get_wordcloud_image('GRP_8')
In [115]:
get_wordcloud_image('GRP_24')
In [151]:
get_wordcloud_image('GRP_12')
In [152]:
get_wordcloud_image('GRP_9')
In [153]:
get_wordcloud_image('less_pop')
Join tokens for TFIDF¶
In [123]:
def merge_tokens(tokens_list):
merge_output = " "
for token in tokens_list:
merge_output = merge_output + " " + token
return merge_output
In [124]:
model_data['final_process_description'] = model_data['tokens_desc_lemmatization'].apply(lambda v: merge_tokens(v))
In [125]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
desc_tfidf = vectorizer.fit_transform(model_data['final_process_description'])
# print(vectorizer.get_feature_names())
# print(desc_tfidf.shape)
In [126]:
print(desc_tfidf.shape)
(8491, 14302)
In [127]:
print(vectorizer.get_feature_names())
['__', '___', '____', '_____', '______', '_______', '________', '_________', '__________', '___________', '____________', '_____________', '______________', '_______________', '________________', '_________________', '__________________', '___________________', '____________________', '_____________________', '_______________________', '_________________________', '___________________________', '________________________________', '_________________________________', '_____________________________________________', '___________________________________________________________________________', '____________na_____________', '___________global_telecom_', '___________na______________', '___________swisscom______________', '__________no_______________', '_________na__', '_________na________________', '_________oct', '________at', '________global_telecom_', '________na___', '________na_________________', '_______na', '_______na_', '_______na____', '_______no__', '_______no____', '_______yes__', '______global_telecom_', '______na__', '______na_____', '______no__', '______no___', '______no_____', '______telecom_vendor_', '______yes_', '______yes__', '______yes___', '_____circuit', '_____global_telecom_', '_____global_telecom_____________________', '_____na__', '_____na___', '_____na____', '_____na______', '_____na____________________', '_____naa___', '_____network', '_____no', '_____no_', '_____no__', '_____no___', '_____no____', '_____no______', '_____noi____', '_____power', '_____slo', '_____telecom_vendor_', '_____x', '_____yes_', '_____yes__', '_____yes___', '_____yes____', '____email_____', '____global_telecom_', '____na___', '____na____', '____na_____', '____na______', '____na_______', '____network', '____no__', '____no___', '____no____', '____no_____', '____no_______', '____power', '____slo', '____slo_____', '____telecom_vendor_', '____x_circuit', '____x_network', '____ye', '____yes__', '____yes___', '____yes____', '____yes_____', '___dir', '___direct', '___email______', '___na___', '___na____', '___na_____', '___na______', '___na________', '___no___', '___no____', '___no_____', '___no______', '___pm', '___x__circuit', '___x__network', '___x__power', '___yes___', '___yes____', '___yes_____', '___yes______', '___yes________', '___yes__circuit', '__dmprmbfdedabcbeaefcadmprmbnamp_', '__email_______', '__na___', '__na____', '__na_____', '__na______', '__na_______', '__na_________', '__no', '__no____', '__no_____', '__no______', '__no_______', '__on', '__primari', '__slo', '__telecom_vendor_', '__x___circuit', '__x___network', '__x__circuit', '__x__network', '__yes__', '__yes___', '__yes____', '__yes_____', '__yes______', '__yes_______', '__yno_____', '_alr_', '_am', '_case', '_foxmail', '_kata', '_kmscan', '_kxmidsga', '_mccoy_', '_mm', '_na___', '_na_____', '_na______', '_na_______', '_no_____', '_no______', '_no_______', '_no________', '_nxd_', '_oper', '_primari', '_re', '_request', '_scan', '_urgent', '_x', '_x____circuit', '_x____network', '_yes_____', '_yes_______', 'aa', 'aaa', 'aab', 'aacbcc', 'aacbccbefb', 'aacfa', 'aacount', 'aad', 'aadb', 'aae', 'aaef', 'aao', 'aaplant_', 'ab', 'abandon', 'abap', 'abarbeiten', 'abb', 'abba', 'abbc', 'abc', 'abca', 'abcdegi', 'abcdri', 'abd', 'abdhtyu', 'abe', 'abend', 'abeoucfj', 'aber', 'abertura', 'abf', 'abff', 'abfragetimeout', 'abgebildet', 'abgebrochen', 'abgelaufen', 'abgeschlossen', 'abgesprochen', 'abgrtyreu', 'abhanden', 'abhandengekommen', 'abhay', 'abhilf', 'abholen', 'abholung', 'abil', 'abl', 'abmelden', 'abod', 'abort', 'about', 'abov', 'abovement', 'abrechnung', 'abreu', 'abrurto', 'absenc', 'absend', 'absent', 'absolut', 'absolvieren', 'abstech', 'abstechprogramdntym', 'abstã', 'abteilung', 'abwfnzwbnvbw', 'abzubrechen', 'abzulesen', 'ac', 'ac_line_interrupt', 'acach', 'acb', 'acbccb', 'acc', 'acccount', 'accdb', 'acce', 'acced', 'accept', 'accept_pass', 'acceptingâ', 'acces', 'acceso', 'accespoint', 'access', 'accid', 'accident', 'accompani', 'accomplish', 'accont', 'accord', 'accound', 'account', 'accountnam', 'accout', 'accross', 'accrual', 'accs', 'accsess', 'acct', 'accur', 'accuraci', 'acd', 'ace', 'acessar', 'acesso', 'acf', 'acgyuna', 'ach', 'achghar', 'achghyardr', 'achim', 'achthyardk', 'achtstelligen', 'ack', 'acknowledg', 'acl_insid', 'acmglkti', 'aconnect', 'acount', 'acqpinyd', 'acquir', 'acrobat', 'across', 'act', 'action', 'actionperform', 'activ', 'active______', 'active_______', 'activesync', 'activex', 'activitiess', 'actual', 'acuvyqnx', 'acwuvpuzdunkhjwjqwvmyqktqtf', 'acxedqjm', 'aczyfqjr', 'ad', 'ada', 'adadd', 'adaef', 'adapot', 'adapt', 'adaptor', 'adbf', 'adc', 'add', 'adddf', 'addiit', 'addin', 'addit', 'addon', 'addr', 'addreess', 'address', 'addressâ', 'adeca', 'adelhmk', 'adfa', 'aditya', 'adiuklhl', 'adjtmlzn', 'adjust', 'admin', 'administr', 'administra', 'administrador', 'administrationsrechten', 'adob', 'adopt', 'adowi', 'adpvilqu', 'adress', 'adressaten', 'adrhtykin', 'adt', 'advanc', 'advantag', 'advic', 'advis', 'advisor', 'adwar', 'adwind', 'adwjfpbreu', 'ae', 'aeae', 'aeb', 'aecd', 'aedwrpvo', 'aedzqlvj', 'aee', 'aeea', 'aef', 'aeftjxo', 'aegpkruc', 'aeithcvp', 'aenderungsantrag', 'aenl', 'aeophctw', 'aeptfbg', 'aero', 'aerospac', 'aerp', 'aes', 'aetwpiox', 'aevzsogn', 'af', 'afab', 'afc', 'afcbrhqw', 'afdceb', 'afdf', 'afe', 'afef', 'afefsano', 'aff', 'affect', 'affili', 'afgdmesz', 'afghtyjith', 'aficio', 'afkstcev', 'afplnyxb', 'afqvyuwh', 'africa', 'afsid_', 'after', 'afterit', 'afternoon', 'afukzhnm', 'afwzehq', 'ag', 'again', 'against', 'agathon', 'agbighyail', 'agdsqbwv', 'agent', 'agentid', 'agfmpyhr', 'agfxelwz', 'aggergryth', 'aghl', 'aghw', 'aghynil', 'aghynilthykurtyar', 'agian', 'agjzikpf', 'agnwfwieszka', 'ago', 'agora', 'agre', 'agreement', 'agrtyw', 'agthynew', 'agvl', 'agvw', 'ahbgjrqz', 'ahdwqrson', 'ahead', 'ahgjsvoq', 'ahjklpxm', 'ahlqgjwx', 'ahmbnsoi', 'ahmet', 'ahost', 'ahrskvln', 'ahsnwtey', 'ahujajtyhur', 'ahydmrbu', 'ahyeqpmx', 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'zeichnungen', 'zeichnungsnumm', 'zeichnungsrahmen', 'zeigen', 'zeigt', 'zeilmann', 'zeit', 'zeitbuchungen', 'zeitdaten', 'zeiterfassung', 'zeiterfassungskarten', 'zeitkonten', 'zeitnachweis', 'zeitpunkt', 'zeitraum', 'zeitweis', 'zeitwirtschaft', 'zekluqim', 'zektaqof', 'zembok', 'zenjimdghtybo', 'zero', 'zeugnis_kunden', 'zevpkogu', 'zf', 'zfburidj', 'zfd', 'zfghsxiw', 'zfiu', 'zfjbpntg', 'zfliqpxm', 'zfovlrah', 'zgdckste', 'zgmdster', 'zgå', 'zh', 'zhang', 'zhengdr', 'zheqafyo', 'zhhtyangq', 'zhm', 'zhothu', 'zhrgtang', 'zhu', 'zhudr', 'zhuyht', 'zhwmifvx', 'zieh', 'ziehen', 'ziel', 'ziewxqof', 'zifujpvr', 'zigioachstyac', 'zihrtyud', 'zikuvsat', 'zip', 'zipcod', 'zipe', 'ziqmkgcw', 'zitec', 'ziv', 'zj', 'zjawqgc', 'zjcsqtdn', 'zjdmftkv', 'zjdmoahr', 'zjihgovn', 'zkaowfrx', 'zkb', 'zke', 'zkea', 'zkevitua', 'zkgfcyvx', 'zkln', 'zklnâ', 'zkwfqagb', 'zla', 'zlettel', 'zlftrkpq', 'zlgmctws', 'zlkmlwdwdade', 'zlnfpuam', 'zlnxswvp', 'zload', 'zlp', 'zlqfptjx', 'zlx', 'zlxcsqdg', 'zlyiwtch', 'zlz', 'zm', 'zmc', 'zmcp', 'zmeo', 'zmgsfner', 'zmkitbsh', 'zmm_bestand_stl', 'zmm_stock_transf', 'zmmdata', 'zmmtaxupd', 'zn', 'znalhivf', 'znbvlygd', 'zncajubh', 'zndgqcux', 'znet', 'znewqgop', 'zneyrlhg', 'zno', 'znq', 'znqcljxt', 'znqlmjvt', 'znr', 'zntc', 'znxcupyi', 'zocjdutp', 'zokivdfa', 'zollerfgh', 'zolnubvq', 'zona', 'zone', 'zopbiufn', 'zopcrshl', 'zor', 'zowemnca', 'zpcokgbj', 'zpd_upload_tim', 'zpdist_programdnti', 'zpfitlyu', 'zpmxgdcw', 'zpononpo', 'zpress', 'zpsr', 'zpsu', 'zpumhlic', 'zpwgoqju', 'zqbgmfle', 'zqbldipk', 'zqbmxdgi', 'zqt', 'zredeploy', 'zrfc', 'zrmlhkyq', 'zrobiä', 'zrpemyab', 'zrvbahym', 'zrwdgsco', 'zscxqdho', 'zscxqdhoalaramdntyan', 'zsd', 'zsd_mexico_inwarehouse_tool_extract', 'zsd_price_field_upd', 'zsdr', 'zsdrshstrud', 'zsdslsum', 'zslugaxq', 'zsluxctw', 'zspvxrfk', 'zsqabokr', 'zssid_', 'zstkagwu', 'ztax', 'ztdgvclp', 'ztdxwpcn', 'ztfn', 'ztnfhiwq', 'ztswnaom', 'zttf', 'ztyhng', 'zu', 'zuehlk', 'zugang', 'zugordnet', 'zugriff', 'zugriffsrecht', 'zuhaus', 'zuhoylt', 'zukommen', 'zulassen', 'zum', 'zumindest', 'zuothryrt', 'zupifghd', 'zur', 'zurtxjbd', 'zurzeit', 'zurã¼ck', 'zurã¼ckgesetzt', 'zurã¼ckgestzt', 'zurã¼cksetzen', 'zurã¼cksetzten', 'zurã¼ckzusetzen', 'zusammen', 'zuschaltung', 'zuspjbtw', 'zustã', 'zuteillisten', 'zuvjqgwa', 'zuvor', 'zuwhpqrc', 'zuwnxdbt', 'zuxcfonv', 'zuyimtsf', 'zvgixwksid_qzzitpqyludriottgkxzayeeyzavgeudngmojgvhdtnqwuiojybnp', 'zvjwleuo', 'zvjxuah', 'zvmesjpt', 'zvnxlobq', 'zvygmnco', 'zw', 'zwar', 'zwei', 'zweit', 'zwip', 'zwirhcol', 'zwischen', 'zwkfzfqhveyrfplvnznkjsmckqwehilqgumjnxnqprprqkdhi', 'zwrypjqv', 'zwutmehi', 'zwwirep', 'zwwzwsnkerplcb', 'zxdtskpw', 'zxobmreq', 'zxvjsipd', 'zyewibop', 'zyjfpgtk', 'zyrnqiav', 'zywoxerf', 'zz', 'zz_mail', 'zzcmpgn_id', 'zzsdspc', 'ªerpæ', 'ªexcel', 'ªfolder＜officeæ', 'ªã', 'ªå', 'ªå¼', 'ªåš', 'ªæœˆæ', 'ªé', '²å', '²åˆ', '²æš', '²ç', '²è', '²é', '³south', '³ã', '³ä', '³äºžbusiness_clientæ', '³æ', '³ç³', '³è', '³é', '³ï¼ÿ', '³ï¼œå', 'µshipment', 'µã', 'µä', 'µå', 'µæ', 'µæº', 'µç', 'µè', 'µé', '¹ã', '¹ãƒ', '¹ä', '¹äº', '¹å', '¹å¼', '¹æ', '¹è', '¹éƒ½ä', '¹ï¼œè', '¹ï¼ˆ', 'ºjavaæ', 'ºlogon', 'ºoutlook', 'ºskype会è', 'ºã', 'ºå', 'ºæ', 'ºæ²', 'ºæœªè', 'ºç', 'ºç³', 'ºçž', 'ºè', 'ºè¾', 'ºé', 'ºï¼œæ', 'ºï¼œè', 'ºï¼šsdguo', 'ºï¼šä¼', '¼psfäº', '¼å', '¼å¾œ', '¼æž', '¼è', '½ä', '½å', '½ï¼', '½ï¼œit', '½ï¼œä', '¾ã', '¾ä', '¾å', '¾åº', '¾åƒ', '¾åˆ', '¾æ', '¾æž', '¾ç', '¾ç½', '¾çž', '¾è', '¾éÿ³é', 'à²', 'ಳà³', 'ಜà³', 'ಞà²', 'à³', 'ೃà²', '∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞', 'ãµes', 'ã¼ber', 'ã¼berblick', 'ã¼berein', 'ã¼bergangsweis', 'ã¼berlegen', 'ã¼bernommen', 'ã¼berprã¼fen', 'ã¼berprã¼ft', 'ã¼berprã¼fung', 'ã¼bersicht', 'ã¼bertagen', 'ã¼bertragen', 'ã¼bertragung', 'ã¼berwachung', 'ã¼dvã', 'ãÿel', 'ãÿigen', 'ãÿt', 'ãƒ', 'ンã', 'ーãƒ', 'ーン', 'ーンã', 'トã', 'ä¹', 'ä¹ÿå', 'ä¹ÿæš', '么åžÿå', '么é', 'äº', '人', '人ä', '人ä¼', '人ä½', '人å', '人å¹', 'ä¼', '会ç', '会è', 'ä½', '作ç', '作ç³', '佞', 'ä¾', 'å²', 'å¹', 'å¹³', 'å¹³ä¼', 'å¹³ï¼', 'åº', 'å¼', 'å¼¹å', 'å½', 'å¾', '徜', '很ç²', 'åÿºã', 'åÿÿä¹', 'åœ', '场é', 'åš', 'åšÿ', 'åž', 'åžÿ', 'åžÿæ', 'åˆ', 'æ²', 'æ³', 'æµ', 'æÿ', 'æœ', '未ç', '机', '机feæ', '机ã', '机ä', '机å', '机å¼', '机æ', '机æ²', '机çƒ', '机è', '机é', '机＜æ', '月æ', 'æš', 'æššä½', 'æššæˆ', 'æž', 'æžœæ', '枈æ', 'æƒ', 'æˆ', 'çª', 'ç²', 'ç³', 'çµ', 'çº', '纳è', '纳é', '纵å', 'ç¼', 'ç½', 'çœ', 'çœÿå', 'çœÿ机ä', 'çœÿ机ä¼', 'çš', 'çž', 'çƒ', 'è²', 'è³', 'è³¼è²', 'è¼', 'è½', 'è½½å', 'è½½åº', 'è½½æ', 'è½½é', '载＜æ', 'è¾', '能ä½', '能å', '能å¼', '能åœ', '能æ', '能æž', '能ç', '能ç¼', '能è', '能é', '骜机æž', 'éÿ³', 'éÿ³ã', 'éÿ³ï¼œskypeä¹ÿæ²', 'éœ', '露çš', 'éšœ', 'éšœã', '障＜', '障＜ã', '障＜å', '障＜è', 'éƒ', '都ä', '都æ', '都éœ', 'ëª', 'ï¼', 'ï¼ÿ', '＜', '＜adob', '＜can', '＜ethicsæ', '＜outlook', '＜vpnæ', '＜vpnçž', '＜ä', '＜ä¹ÿæ', '＜äº', '＜ä½', '＜å', '＜å¼', '＜åœ', '＜æ', '＜æœ', '＜æš', '＜æž', '＜æˆ', '＜ç', '＜ç½', '＜çž', '＜è', '＜能å', '＜é', '＜éœ', '＜都ä', '：', '：å', '：＜åº', '：（crmæ', '＞ä', '＞è', '（fi', '（ä½', '（弚ç', '（è½', '～', 'ïƒ', 'ð²ð', 'ðµð', 'ðµð²ð³ðµð½ð', 'ðµð¼', 'ðµð¼ð', 'ðµð½ð', 'ðµð½ð¾', 'ðµñ', 'ðºð¾ð¼ñƒ', 'ð½ð', 'ð½ð½ð¾ð³ð¾', 'ð¾', 'ð¾ð²ð', 'ð¾ð¾ð', 'ð¾ñ', 'ñƒð²ð', 'ÿjavaæ', 'ÿã', 'ÿä', 'ÿäº', 'ÿ人ç', 'ÿå', 'ÿåº', 'ÿæ', 'ÿæœ', 'ÿæƒ', 'ÿæˆ', 'ÿç', 'ÿçš', 'ÿè', 'ÿé', 'œvpnå', 'œã', 'œä', 'œå', 'œåœ', 'œåˆ', 'œæ', 'œæˆ', 'œç', 'œçš', 'œçž', 'œè', 'œèª', 'œé', 'œï¼œæ', 'šipåœ', 'šã', 'šä', 'šäº', 'šä¼', 'šä¼šç', 'šå', 'šå¹', 'šå¼', 'šå¾œ', 'šåš', 'šåž', 'šæ', 'šæµ', 'šæœÿå', 'šç', 'šçÿ', 'šçš', 'šè', 'šé', 'šï¼œå', 'šï¼œè', 'žadvanc', 'žanmeld', 'žapptc', 'žauftrag', 'žbaut', 'žbriefkopf', 'ždaypay', 'ždw', 'žem', 'žgast', 'žmbb', 'žmore', 'žnew', 'žnot', 'žpr', 'žskype', 'žspecial', 'žtransfereinheit', 'žtravel', 'žvon', 'žvw', 'žä', 'žä¹', 'žä¹ˆè', 'žå', 'žæ', 'žæž', 'žç', 'žç½', 'žçš', 'žè', 'žè½', 'žé', 'žï¼œoutlookæ', 'žï¼œä', 'žï¼œå', 'žï¼œæ', 'žï¼œæœ', 'žï¼œé', 'žï¼ˆä', 'ƒã', 'ƒäº', 'ƒå', 'ƒåˆ', 'ƒæ', 'ˆã', 'ˆå', 'ˆæ', 'ˆæœ', 'ˆæœºç', 'ˆç', 'ˆé', 'ˆï¼œè']
In [128]:
# English we remove non ascii
import re
model_data['final_process_description'] = model_data['final_process_description'].apply(
lambda v: re.sub(r'[^\x00-\x7F]+',' ', v))
In [129]:
vectorizer = TfidfVectorizer()
description_vectorizer = vectorizer.fit_transform(model_data['final_process_description'])
In [130]:
print(description_vectorizer)
(0, 9819) 0.16290412326458273 (0, 230) 0.14517713319384068 (0, 1203) 0.07234039067836692 (0, 5137) 0.16961204382945785 (0, 11459) 0.12991802184803924 (0, 2278) 0.17442299622255303 (0, 1795) 0.19975247046691277 (0, 11627) 0.13708545316376802 (0, 363) 0.18047110652259055 (0, 8641) 0.11076563684486455 (0, 9812) 0.11681114506517465 (0, 605) 0.1925811145465633 (0, 322) 0.1720326144635177 (0, 5659) 0.07434660362459442 (0, 11463) 0.23506702646466335 (0, 2007) 0.2912670360877012 (0, 7838) 0.2723382795393243 (0, 7232) 0.15432965443366758 (0, 3654) 0.17589519479881435 (0, 2881) 0.164427567531512 (0, 12137) 0.357670596641978 (0, 12309) 0.16802764210760893 (0, 5882) 0.22580119295357695 (0, 6983) 0.4224844007731926 (1, 6393) 0.1702024176550869 : : (8489, 4507) 0.34387803557710434 (8489, 346) 0.3116791627669808 (8489, 3850) 0.26661354088148936 (8489, 7163) 0.47139124191103854 (8489, 4293) 0.2888336989394089 (8489, 12162) 0.2566348261292854 (8489, 10457) 0.19790648909951003 (8489, 8069) 0.1475231535335667 (8489, 7939) 0.16598782984296026 (8489, 268) 0.13768599363983863 (8489, 11980) 0.12989903747956175 (8489, 1203) 0.16117278534490456 (8489, 11627) 0.15271167400040184 (8489, 11463) 0.17457470643429426 (8490, 9069) 0.41574149493070145 (8490, 12324) 0.41574149493070145 (8490, 7389) 0.41574149493070145 (8490, 6693) 0.3488903875340773 (8490, 1297) 0.1924667341118502 (8490, 2153) 0.1875077494393383 (8490, 4235) 0.31339944546467435 (8490, 8014) 0.21357175506748313 (8490, 10572) 0.27179329020877496 (8490, 590) 0.174425610181312 (8490, 8685) 0.19854957253891573
In [131]:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
def create_bag_of_words(X):
vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
stop_words = 'english', ngram_range = (1,3), max_features = 10000)
train_data_features = vectorizer.fit_transform(X)
train_data_features = train_data_features.toarray()
# tfidf transform
tfidf = TfidfTransformer()
tfidf_features = tfidf.fit_transform(train_data_features).toarray()
# Get words in the vocabulary
vocab = vectorizer.get_feature_names()
return vectorizer, vocab, train_data_features, tfidf_features, tfidf
In [132]:
vectorizer, vocab, train_data_features, tfidf_features, tfidf = create_bag_of_words(
model_data['final_process_description'])
Logistic Regression¶
In [133]:
from sklearn.model_selection import train_test_split
X = model_data['final_process_description']
y = model_data['updated_target']
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25)
In [134]:
vectorizer, vocab, train_data_features, tfidf_features, tfidf = create_bag_of_words(X_train)
In [135]:
def train_logistic_regression(features, label):
print ("Training the logistic regression model...")
from sklearn.linear_model import LogisticRegression
ml_model = LogisticRegression(C = 100,random_state = 0)
ml_model.fit(features, label)
print ('Finished')
return ml_model
In [136]:
logistic_model = train_logistic_regression(tfidf_features, y_train)
Training the logistic regression model... Finished
/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
In [137]:
test_data_features = vectorizer.transform(X_test)
# Convert to numpy array
test_data_features = test_data_features.toarray()
In [138]:
test_data_tfidf_features = tfidf.fit_transform(test_data_features)
# Convert to numpy array
test_data_tfidf_features = test_data_tfidf_features.toarray()
In [140]:
predicted_train_y = logistic_model.predict(tfidf_features)
correctly_identified_train_y = predicted_train_y == y_train
accuracy = np.mean(correctly_identified_train_y) * 100
print ('Training Accuracy = %.0f%%' %accuracy)
Training Accuracy = 94%
In [141]:
predicted_y = logistic_model.predict(test_data_tfidf_features)
correctly_identified_y = predicted_y == y_test
accuracy = np.mean(correctly_identified_y) * 100
print ('Test Accuracy = %.0f%%' %accuracy)
Test Accuracy = 67%
In [150]:
from sklearn.metrics import classification_report
print(classification_report(y_test, predicted_y, target_names=targets_list))
precision recall f1-score support
GRP_0 0.75 0.91 0.82 950
GRP_1 0.50 0.33 0.40 9
GRP_3 0.88 0.51 0.65 41
GRP_4 0.33 0.50 0.40 2
GRP_5 0.54 0.58 0.56 64
GRP_6 0.58 0.54 0.56 39
GRP_7 0.73 0.23 0.35 35
GRP_8 0.80 0.40 0.53 10
GRP_9 0.64 0.36 0.46 25
GRP_10 1.00 0.95 0.98 22
GRP_11 0.48 0.43 0.45 23
GRP_12 0.39 0.37 0.38 54
GRP_13 0.56 0.54 0.55 61
GRP_14 0.00 0.00 0.00 9
GRP_15 0.00 0.00 0.00 6
GRP_16 0.00 0.00 0.00 10
GRP_17 0.43 0.75 0.55 4
GRP_18 0.90 0.87 0.88 70
GRP_19 0.76 0.37 0.50 35
GRP_2 0.29 0.20 0.24 10
GRP_20 0.00 0.00 0.00 5
GRP_21 0.67 0.13 0.22 15
GRP_22 0.59 0.65 0.62 20
GRP_23 0.50 0.39 0.44 56
GRP_24 0.29 0.15 0.20 13
GRP_25 0.22 0.12 0.16 16
GRP_26 0.35 0.29 0.32 24
GRP_27 0.50 0.30 0.38 23
GRP_28 0.33 1.00 0.50 1
GRP_29 0.00 0.00 0.00 6
GRP_30 0.50 0.50 0.50 6
GRP_31 0.58 0.27 0.37 26
GRP_33 0.67 0.33 0.44 12
GRP_34 0.86 0.60 0.71 10
less_pop 0.40 0.25 0.31 8
GRP_36 1.00 0.50 0.67 2
GRP_37 0.00 0.00 0.00 6
GRP_39 0.33 0.11 0.17 9
GRP_40 0.00 0.00 0.00 9
GRP_41 0.73 0.42 0.53 38
GRP_42 0.00 0.00 0.00 3
GRP_44 0.00 0.00 0.00 2
GRP_45 0.66 0.46 0.54 54
GRP_47 0.00 0.00 0.00 4
GRP_48 0.25 0.17 0.20 6
GRP_50 0.00 0.00 0.00 3
GRP_53 0.64 0.27 0.38 26
GRP_60 0.54 0.90 0.67 158
GRP_62 0.48 0.18 0.26 62
GRP_65 0.42 0.24 0.30 21
accuracy 0.67 2123
macro avg 0.44 0.34 0.36 2123
weighted avg 0.64 0.67 0.63 2123
/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
In [ ]: